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Type 2 diabetes mellitus classification using predictive supervised learning model
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DOI：10.1007/s00500-023-08726-4.png)
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Abstract
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There is a tremendous increase in severe cases of type 2 diabetes in the day today's life. Proper assessment of the disease is very important to save society. Many prediction models are helpful in identifying type 2 diabetes, at the same time each and every model varies based on the performance measures. Various kinds of algorithms such as decision tree, logistic regression, KNN, random forest algorithm are used to identify type 2 diabetes. At this juncture, the Ensemble approach is applied by applying AdaBoost algorithms for the classification of type 2 diabetes. Here, the proposed methodology of the paper is to implement an ensemble approach of machine learning to receive a better efficiency when compared to other existing algorithms for the classification of type 2 diabetes. When compared to all other algorithms, this ensemble approach shows an efficiency of 83%. The accuracy is calculated based on various performance measures.
Keywords:
Supervised learning
Diabetes mellitus
Machine leaning
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Discussion: Theoretical investigation of the time-dependent behaviour of rockfill.png)
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